Thursday, February 08, 2007

Congrats to last.fm who just announced its first content agreement with a major label.

The deal will let Last.fm'srapidly growing community have access to Warner's amazing roster of artists through their free streaming radio service and tee up a premium subscription radio service.

Last.fm is growing like crazy, with over 15m monthly active users now experiencing the joys of discovering new music through the recommendations of other community members. Making it easy for people to access major label content will only make the service more valuable to its users and easier for labels to promote both new and catalogue product to consumers - so great news for everyone.

This service is particularly close to my heart (and not just because I am a small investor) as I've been dabbling in the entertainment recommendations space for over ten years now.

First with Firefly, where we launched the web's first music recommendation service (later sold to Launch, now Yahoo Music) and then again with Video Island (now Lovefilm) where we applied similar principles to movie recommendations to build Europe's largest online DVD rental service.

There is no doubt that at Firefly we were way too early and the Internet was too immature a medium for the real power of recommendations to take off, but in the last ten years both Amazon and Netflix have done an amazing job popularizing collaborative filtering and making recommendations a central plank of consumer discovery.

Amazon has elevated the application of datamining to help consumers decide what products to buy to an art form. Netflix values the approach so much it's offered a $1m prize to anyone who can improve their approach to movie discovery.

More often than not the weapons of datamining have been wielded by companies on behalf of companies - they mined to see how to sell more. Companies in the future will put these weapons to work for consumers.

Last.fm is at one of the new businesses at the vanguard of taking discovery to the next level.

One thing which is particularly nice about their approach is the fact that through their scrobbling software (which attaches to your media player), they make it so easy for you to contribute to and benefit from the rest of the community.

We wrestled with the earliest applications of social information filtering at Firefly and one of the big challenges was always about how to best motivate explicit (ie. a rating) and weight implicit (ie. an observed behaviour) data. There is no doubt implicit data collection is much easier on the user, it takes no time, all you have to do is agree to be observed and feel comfortable that in return your information will get great value and your privacy will be protected. NicBrisbourne has an interesting post on this subject.

Last.fm works because people trust the service to watch what they are listening to they get discover new things -- clearly this works beautifully with music, but there is no doubt with the emergence of other digitally based services we will see more and more of this paradigm. My old friend Seth Goldtsein, as usual, is really pushing the envelope here with his work on Attention Trust and Roots Market.

side note: another cool thing about last.fm is their integration with Skype. All the communications service (Skype, Google, AIM, Yahoo! and Messenger) are now opening up to 3rd parties here are going to be amazing opportunities to build great application on top of platforms which come with presence, contacts, voice, IM and video built in. What an amazing platform for new applications to be built around.

Like with Last.fm, obviously this can work with people's explicit knowledge with video (think where Joost can go) and it is already working to some degree without people's knowledge in online advertising and the current vogue for behavioural targeting.

We are scratching the surface of how we will be able to leverage the computational power now available to us for the analysis of vast swathes of comples data to benefit consumers and help them improve their decisions. One of the best examples of a service helping people make better decisions through the application of hard core math and computation is Farecast -- delivering an amazingly valuable view of airfare information to help people decide when to buy tickets.

3 Comments:

Thanks Saul. A question I am thinking about a lot at the moment is how to get these services to critical mass. Absent expensive development of algorithms even with machine based recommendations you need a good dataset to deliver real benefit.

Last.fm has already made it, but it is increasingly difficult for new services to do the same.

Nick - Agree this is all about the volume of data. Although I have to say at Firefly, we found that even with a small dataset you could do pretty good recommencations.

I think the key issue is how to motivate people to share information. Last.fm does a great job of data collection with the scrobbler and I think the challenge for other services is to make sure that data collection is similarly easy and valuable.

My concern with clickstream data collection is that you have to really show clear value for consumers not to feel spied on - but someone will figure out how to do this well. Maybe's its something for the communications clients to build in?